1. Technical Field
The present invention relates to medical image processing, and more particularly, to a system and method for determining a size of an airway lumen and a thickness of an airway wall.
2. Discussion of the Related Art
Pulmonary diseases such as bronchiectasis, asthma and emphysema are characterized by abnormalities in airway dimensions, including airway wall thickness and lumen diameter. Multi-slice computed tomography (MSCT) has become one of the primary means to depict these abnormalities as the availability of high-resolution near-isotropic data makes it possible to evaluate airways at angles that are oblique to a scanning plane. However, clinical evaluation of the airways is generally limited to visual inspection.
Recently, automated methods have been proposed that are based on the extraction of a bronchial tree model and its segmentation. One method described in A. P. Kiraly, J. M. Reinhardt, E. A. Hoffman, G. McLennan, W. E. Higgins, “Virtual Bronchoscopy for Quantitative Airway Analysis” SPIE Medical Imaging, 2005: Physiology, Function, and Structure from Medical Images, A. Amini and A. Manduca, eds, SPIE Proceedings vol. 5746, February, 2005, relies on a full-width half-max method to determine the radii of the inner and outer walls. In another method described in R. Wiemker, T. Blaffert, T. Bulow, S. Renisch, C. Lorenz, “Automated Assessment of bronchial lumen, wall thickness, and bronchoarterial tree using high resolution CT”, CARS 2004: 967-972, measurements based on radial derivatives are used. However, since no correlation is enforced between individual radius measurements with these methods, there exists the potential for errors near bifurcations and at walls with nearby blood vessels. For example, a reading of 1 mm can be obtained in one direction while a reading of 2 mm can be obtained in a nearby direction.
To reduce the possibility of errors near bifurcations and at walls with nearby blood vessels, a correlation enforcement method was introduced in K. Li, X. Wu, D. Z. Chen, M. Sonka, “Efficient Optimal Surface Detection: Theory, Implementation and Experimental Validation,” SPIE Medical Imaging 2004: SPE Proceedings vol. 5370, February 2004. In this method, a plane is fit to reformatted data and an optimal surface is determined. However, here, only the lumen is measured.